Plan Foncier Rural Impact Evaluation 2022 - Benin

Abstract
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Land is an economic asset that serves multiple important purposes: residential, agricultural, and communal (grazing lands, forests, water bodies, public infrastructure). Tenure security is crucial in ensuring poverty reduction, food security and equity. Farmers who lack secure land rights are less likely to carry out essential yield-improving investments in their land as the insecurity prevents them from committing to long-term plans.\

The Promotion d’une Politique Foncière Responsable (ProPFR), is a GIZ funded programme to improve the land tenure security of households on customary land in the Borgou department of northern Benin.

The main objectives of ProPFR are:
a) Improvement of institutional conditions and procedures to provide secure land rights (PFR, ADC, formalization of user agreements, group rights) and reducing land conflicts by establishing local conflict mediation institutions.
b) Participation of civil society in the formulation and implementation of the legal framework for land
c) Inclusion of private agricultural investors and raising their awareness for responsible land policies.

Study Objectives:
The PFR activities to be evaluated at end-line consists mainly of demarcation and registration of land parcels (under customary tenure) as Titre Foncier or an Attestation de Droit Coutumière. The impact evaluation aims to quantify and analyse impact of these interventions on productivity and food security disaggregated by target groups and gender.

The research questions to be answered after the endline data collection are:
1. Do PFRs (or ADCs) contribute to a perception of greater land tenure security?
2. Does improved tenure security lead to a growth in agricultural investment and/or changes to management of land?
3. Do PFRs improve access to land and rights over land among marginalised groups (women, youth and migrants)?
4. Do PFRs lead to an increased number of land transactions?
5. Does increased land security address existing constraints on land markets and lead to more efficient allocation of land resources and thereby an increase in productivity?
6. Do property rights and improved user rights result in better access to credit, possibly allowing for income diversification and thus increasing household welfare?
7. Do the new arrangements put in place during the implementation of the PFRs facilitate the resolution of land conflicts, or even prevent the emergence of these land conflicts?

Geographic coverage
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These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Analysis unit
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- Villages
- Households

Kind of data
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Sample survey data [ssd]

Sampling procedure
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The impact evaluation consists of gender and youth disaggregated data collection at baseline, before the start of the intervention, in both the treatment and control villages. Endline data was collected at least 2 growing seasons after issuing of documentation to farmers.

The sample consisted of 2,626 households, which were taken from 52 villages of the four municipalities selected for the implementation of a Plan Foncier Rural (PFR), or rural landholding plans, these were the treatment villages and 27 control villages that did not benefit from a PFR.

Selection of Sample Areas
The treatment villages were assigned by the ProPFR team in geographic clusters. The assignment of control villages followed this geographic clustering, also using further village level data with the aim of finding similar villages to maximize comparability.

These clusters were spread across the communes of Bembéréké, Sinendé and Kalalé in the north and Tchaourou in the south of the department of Borgou.

Villages were selected from 11 geographical clusters of villages facing similar issues, allowing easier logistical planning for the rollout of the PFRs.

Villages selected to be part of the programme had the following characteristics;
• Bordering/near to a classified national forest
• At high risk of land grabbing,
• The presence of another GIZ supported SEWOH project1
• Agropastoral areas (particularly the presence of transhumance –cattle driving - corridors)

But should not have the following:
• Villages bordering Nigeria, within the band of increased security;
• MCA intervention with a PFR; and
• Suffered serious conflict which could block the realisation of a PFR, or where a PFR may reignite past conflicts.
These characteristics alongside the logistical requirement to select villages in clusters presented the first challenge in selecting suitable comparison villages to measure the impact of the ProPFR programme. Clustering meant that villages selected for comparison should be near the clusters to be comparable but given the typical geography of villages in northern Benin, in that most people live in the village centre rather than spread evenly with sufficient density at the village boundary, and the lack of clearly defined village boundaries, a geographic discontinuity could not be exploited.

The second challenge in selecting comparison villages arose due to a change in the village definitions in 2013, when Benin changed from 3,758 to 5,290 villages which is often referred to as the “nouveau découpage”. Some old villages were split but there are no clearly defined village boundaries for the new set of villages. ProPFR selected from among the new villages, so the control villages also needed to be selected from this list. Given that the last census was collected prior to this new definition of villages, no data about the villages existed that could easily be used in matching villages to those selected for the ProPFR.

Due to this lack of data on the characteristics of the people residing in the villages, Geographical Information Systems (GIS) data were used to match each of the treatment PFR villages to a control village. Villages which were previously included in the MCA’s wave of PFRs were excluded from our study due to the difficulty in separating the effects of the two programs (MCA vs ProPFR).

For each PFR village, a buffer of 20km was drawn and the union constructed for each cluster. Within this area, other villages were considered as a potential control village. Of the selection criteria, the only one applicable from GIS data is the proximity to a national forest. Where villages were close to a national forest, we attempted to match it with a control village also close to a national forest.

The additional criteria on which villages were matched were the proximity to a main road (as classified by the Open Street Map shapefiles for roads) and the number of buildings in the central agglomeration of a village. Main roads are used as a proxy for access to markets and thereby potentially income levels.

The size of a village and the amount of land which can be used around it will be influenced by the size of the population as well as the presence of national forests.

This strategy is similar to a Coarsened Exact Matching (CEM) strategy (see Blackwell et al, 2009), in which key characteristics are reduced (perhaps from continuous variables) to a small number of categories and matched with one another exactly.

In our selection of villages, one control village was selected for each treatment village based on the key characteristics, defined as proximity to national forests (5km) and main roads (1km), and having a similar number of buildings (within 1km of the central point).

For a small number of villages, we faced an issue of common support, meaning there were no exact matches on the key characteristics. In this case other nearby villages were selected which fulfilled as many of these characteristics as possible.

Data were collected on a wide range of variables following the theory of change, which states that the improvements in institutions and the PFRs may lead to improved perceived land tenure security and improved access to land for women and young men through the activities carried out by the ProPFR team.

This perceived land tenure security is often seen as key to agricultural investments and thereby food security in the long term, as it allows long-term planning. The issuing of official documentation provides collateral for a loan should households wish to borrow and invest in productive activities or smooth consumption.

Mode of data collection
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Computer Assisted Personal Interview [capi]

Research instrument
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The Survey comprised two questionnaires, namely;
1. Household Questionnaire:
Which comprised 17 modules with 19 rosters. Modules include household members, employment and enterprises, durable goods, housing, census of non-agricultural plots, agricultural plots, land donations, land sales, land losses, perceptions on land tenure, participation in PFR, loans, food security, young men and women.

2. Community (village) questionnaire:
The community survey was administrated to each village in the form of small group interviews to collect information on the socio-economic characteristics of these villages, local land tenure structures and practices, and local prices on agricultural inputs and production. The questionnaire was organized in 9 modules: characteristics of the survey participants, land tenure, land use, land market, land conflicts, other village structures and interventions, agriculture, PFR, and village chief. The characteristics of the participants were recorded in a separate roster.

Cleaning operations
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Various consistency checks were performed to ensure data quality, including systematic reports of contradictory answers and of extreme values. The data were also examined for missing information for required variables, and sections. Any problems found were then reported back to the supervisors where corrections were then made.

Response rate
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Roughly about 2,600 households were successfully interviewed at endline, representing a 90% response rate for the PFR villages and 89% for the control villages. The main reason for non-response was due to households moving out of the village (5.8% and 6.9% of households in PFR and control villages respectively). In total only 11 households from baseline refused to be interviewed at endline. There are no systematic differences in household level attrition between PFR and control villages.

土地作为一种经济资产，承担着多重重要功能：住宅、农业和公共（放牧地、森林、水域、公共基础设施）。土地权益的保障对于减贫、粮食安全和公平至关重要。缺乏稳定土地权利的农民，由于这种不稳定性阻止了他们对长期计划做出承诺，因此不太可能在其土地上实施提高产出的基本投资。

《促进负责任土地政策》（ProPFR）是由德国国际合作机构（GIZ）资助的一项计划，旨在改善贝宁北部博尔古省传统土地上家庭的土地权益。

ProPFR的主要目标包括：
a) 改善提供稳定土地权利的制度和程序（PFR、ADC、用户协议的正式化、集体权利）以及通过建立地方冲突调解机构来减少土地冲突。
b) 公民社会参与土地法律框架的制定和实施。
c) 包含私人农业投资者并提高他们对负责任土地政策的认识。

研究目标：
在项目结束时对PFR活动进行评估，主要涉及土地地块（在传统土地权属下）的划界和登记为地权证书或习惯权证明。影响评估旨在量化和分析这些干预措施对生产力和粮食安全的影响，按目标群体和性别进行细分。

在数据收集结束时需要回答的研究问题包括：
1. PFR（或ADC）是否有助于增强对土地权益稳定性的认识？
2. 改善的土地权益是否会导致农业投资的增长和/或土地管理的变化？
3. PFR是否改善了边缘群体（妇女、青年和移民）对土地和土地权利的获取？
4. PFR是否导致土地交易数量的增加？
5. 增加的土地安全性是否解决了现有土地市场的约束，并导致土地资源的更有效分配，从而提高生产力？
6. 财产权和改进的使用权是否导致更好地获取信贷，可能允许收入多元化，从而提高家庭福利？
7. 在PFR实施过程中建立的新安排是否有助于解决土地冲突，甚至防止这些土地冲突的发生？

地理覆盖范围：
这些集群分布在博尔古省北部的本梅雷凯、辛恩代和卡拉莱市镇以及南部的塔乔鲁。

分析单位：
- 村庄
- 家庭

数据类型：
样本调查数据 [ssd]

抽样程序：
影响评估包括在干预开始前、基线阶段，在处理组和对照组村庄进行性别和青年细分的数据收集。在发放文件给农民后至少2个生长季节后收集终点数据。

样本由2,626户家庭组成，这些家庭来自为实施农村土地持有计划（PFR）而选择的四个市的52个村庄，这些是处理村庄和27个未从PFR中受益的对照组村庄。

样本区域的选择：
处理村庄由ProPFR团队在地理集群中分配。对照组村庄的分配遵循这种地理集群，也使用进一步的村庄级数据，目的是找到类似的村庄以最大化可比性。

这些集群分布在博尔古省北部的本梅雷凯、辛恩代和卡拉莱市镇以及南部的塔乔鲁。

从面临类似问题的11个地理集群村庄中选择村庄，以便更容易地规划PFR的推广。

纳入计划中的村庄具有以下特征；
• 与国家森林相邻/靠近
• 非常容易发生土地抢夺
• 存在另一个由GIZ支持的SEWOH项目1
• 耕牧地区（尤其是存在迁徙放牧-牛群迁移-走廊）

但不应该具有以下特征；
• 与尼日利亚接壤的村庄，在加强安全的区域内；
• 进行过PFR的MCA干预；
• 经历了严重冲突，这可能会阻碍PFR的实现，或者PFR可能会重新点燃过去的冲突。

这些特征以及选择集群村庄的物流要求，是选择合适的比较村庄来衡量ProPFR计划影响的第一大挑战。集群意味着选择的比较村庄应该靠近集群以进行比较，但鉴于贝宁北部村庄的典型地理特征，即大多数人住在村庄中心而不是均匀地分布，且村庄边界没有明确界定，因此无法利用地理不连续性。

选择比较村庄的第二个挑战是由于2013年村庄定义的变化，当时贝宁从3,758个村庄增加到5,290个村庄，这通常被称为“新划分”。一些旧村庄被拆分了，但新的一组村庄没有明确界定的村庄边界。ProPFR从新村庄中选择，因此对照组村庄也必须从这份名单中选择。鉴于最后一次人口普查是在这个新的村庄定义之前收集的，因此没有关于村庄的数据可以轻松用于将村庄与ProPFR选择的村庄相匹配。

由于缺乏关于居住在村庄中的人的特征的数据，因此使用了地理信息系统（GIS）数据将每个处理PFR村庄与一个对照组村庄相匹配。由于难以区分两个计划（MCA与ProPFR）的影响，因此排除了以前包括在MCA的PFR波中的村庄。

对于每个PFR村庄，绘制了20公里的缓冲区，并为每个集群构建了并集。在此区域内，其他村庄被视为潜在的对照组村庄。从选择标准中，唯一适用于GIS数据的是靠近国家森林的邻近性。在村庄靠近国家森林的情况下，我们试图将其与一个也靠近国家森林的对照组村庄相匹配。

在匹配村庄时考虑的附加标准是靠近主要道路（由Open Street Map道路形状文件分类）和村庄中心聚集区的建筑数量。主要道路被用作市场准入和市场水平的代理。

村庄的大小以及围绕其可用的土地量将受到人口规模以及国家森林存在的影响。

这种策略类似于粗略精确匹配（CEM）策略（参见Blackwell等人，2009年），其中关键特征被减少（可能是从连续变量中）到少量类别，并相互精确匹配。

在我们选择村庄时，根据关键特征，为每个处理村庄选择了一个对照组村庄，这些关键特征定义为靠近国家森林（5公里）和主要道路（1公里），并且具有相似的建筑数量（在中心点的1公里范围内）。

对于少数村庄，我们面临共同支持的难题，即在这些关键特征上没有确切的匹配。在这种情况下，选择了其他附近的村庄，这些村庄尽可能地满足了这些特征。

根据变革理论，在数据收集过程中收集了广泛变量的数据，该理论认为，制度和PFR的改善可能导致感知的土地权益稳定性的提高，以及通过ProPFR团队开展的活动，改善妇女和年轻男性对土地的获取。

这种感知的土地权益稳定性通常被视为农业投资和长期粮食安全的关键，因为它允许长期规划。发放官方文件为家庭提供贷款的抵押品，如果家庭希望借款并投资于生产活动或平滑消费。

数据收集方式：
计算机辅助个人访谈 [capi]

研究工具：
调查包括两个问卷，即；
1. 家庭问卷：包括17个模块和19个清单。模块包括家庭成员、就业和企业、耐用消费品、住房、非农业地块普查、农业地块、土地捐赠、土地销售、土地损失、对土地权益的看法、参与PFR、贷款、粮食安全、青年和妇女。
2. 社区（村庄）问卷：社区调查以小组访谈的形式对每个村庄进行管理，以收集有关这些村庄的社会经济特征、当地土地权属结构和实践、以及当地农业投入品和生产的价格信息。问卷组织在9个模块中：调查参与者的特征、土地权益、土地利用、土地市场、土地冲突、其他村庄结构和干预措施、农业、PFR和村长。参与者的特征记录在一个单独的清单中。

数据清洗操作：
执行了各种一致性检查以确保数据质量，包括系统地报告矛盾答案和极端值。还检查了所需变量和部分的数据缺失情况。然后，将发现的问题报告给主管，然后进行更正。

响应率：
大约有2,600户家庭在终点成功接受访谈，代表PFR村庄的90%响应率和对照组村庄的89%响应率。非响应的主要原因是因为家庭搬出村庄（PFR和对照组村庄中分别有5.8%和6.9%的家庭）。在基线和终点之间，只有11户家庭拒绝接受访谈。在PFR和对照组村庄之间没有系统性的家庭层面流失差异。